| """ |
| STER — few-shot SAMPLE-EFFICIENT combined head (GPU). |
| |
| The prototype/cosine heads win only at K=5; trees recover by K=10 but overfit at |
| very low K. The documented flagship "safety net" is: augment the raw-ratio vector |
| with the WM identity-relation vector and feed a SAMPLE-EFFICIENT linear model |
| (L2-regularised LogisticRegression) instead of a tree. LR needs far fewer labels |
| than Bagging, so it should hold the K<=10 regime while combined features add signal. |
| |
| Compares at K in {5,10,20,50}: |
| raw_bagging (baseline) | raw_lr | wm_lr | combo_lr (ours) | combo_lr+wmcos_fusion |
| """ |
| import os, json, numpy as np, torch, torch.nn as nn, torch.nn.functional as F |
| from sklearn.ensemble import BaggingClassifier |
| from sklearn.linear_model import LogisticRegression |
| from sklearn.preprocessing import StandardScaler |
| from sklearn.metrics import f1_score |
| from scipy.stats import rankdata |
|
|
| NPZ="/root/ster/data/ster_wm_vectors.npz"; OUT="/root/ster/exp/ster_combo_results.json" |
| DEV="cuda" if torch.cuda.is_available() else "cpu"; torch.manual_seed(1); np.random.seed(1) |
| z=np.load(NPZ,allow_pickle=True); lod12,lod22,candX,indexX=z["lod12_X"],z["lod22_X"],z["cand_X"],z["index_X"] |
| trP,trY,teP,teY=z["train_pairs"],z["train_y"],z["test_pairs"],z["test_y"]; DIM=lod12.shape[1] |
| sc=StandardScaler().fit(np.vstack([lod12,lod22,candX,indexX])) |
| def T(x): return torch.tensor(sc.transform(x),dtype=torch.float32,device=DEV) |
| L12,L22,CAND,INDEX=T(lod12),T(lod22),T(candX),T(indexX) |
| class Enc(nn.Module): |
| def __init__(s,d): |
| super().__init__(); s.net=nn.Sequential(nn.Linear(d,64),nn.GELU(),nn.Linear(64,64),nn.GELU(),nn.Linear(64,32)) |
| def forward(s,x): return F.normalize(s.net(x),dim=-1) |
| def info_nce(a,b,tau=0.1): |
| lg=a@b.t()/tau; lab=torch.arange(a.size(0),device=a.device); return .5*(F.cross_entropy(lg,lab)+F.cross_entropy(lg.t(),lab)) |
| enc=Enc(DIM).to(DEV); opt=torch.optim.Adam(enc.parameters(),1e-3,weight_decay=1e-5) |
| for ep in range(400): |
| p=torch.randperm(L12.size(0),device=DEV) |
| for i in range(0,L12.size(0),256): |
| b=p[i:i+256] |
| if b.numel()<8: continue |
| loss=info_nce(enc(L12[b]),enc(L22[b])); opt.zero_grad(); loss.backward(); opt.step() |
| enc.eval() |
| with torch.no_grad(): ec=enc(CAND).cpu().numpy(); ei=enc(INDEX).cpu().numpy() |
|
|
| def relvec(P): |
| a,b=ec[P[:,0]],ei[P[:,1]]; return np.concatenate([np.abs(a-b),a*b],1) |
| def ratio(P): |
| a,b=candX[P[:,0]],indexX[P[:,1]] |
| with np.errstate(divide='ignore',invalid='ignore'): r=np.where(b!=0,a/b,1000.0) |
| return np.clip(np.round(r,3),None,1000.0) |
| R_tr,R_te=relvec(trP),relvec(teP); raw_tr,raw_te=ratio(trP),ratio(teP) |
| cos_te=np.sum(ec[teP[:,0]]*ei[teP[:,1]],1) |
| |
| rs=StandardScaler().fit(raw_tr); raw_tr_s,raw_te_s=rs.transform(raw_tr),rs.transform(raw_te) |
| Rs=StandardScaler().fit(R_tr); R_tr_s,R_te_s=Rs.transform(R_tr),Rs.transform(R_te) |
| combo_tr=np.concatenate([raw_tr_s,R_tr_s],1); combo_te=np.concatenate([raw_te_s,R_te_s],1) |
|
|
| def fit_pred(Xtr,ytr,Xte,kind): |
| if kind=="bag": m=BaggingClassifier(n_estimators=50,random_state=1) |
| else: m=LogisticRegression(C=0.5,max_iter=2000,class_weight="balanced") |
| m.fit(Xtr,ytr); return m.predict_proba(Xte)[:,1] |
|
|
| Ks,NDRAW=[5,10,20,50],15 |
| pos=np.where(trY==1)[0]; neg=np.where(trY==0)[0]; report={} |
| for K in Ks: |
| keys=["raw_bagging","raw_lr","wm_lr","combo_lr","combo_lr_wmcos_fusion"] |
| acc={k:[] for k in keys} |
| for rep in range(NDRAW): |
| r=np.random.RandomState(400+rep) |
| pidx=r.choice(pos,K,replace=False); nidx=r.choice(neg,min(2*K,len(neg)),replace=False) |
| idx=np.concatenate([pidx,nidx]); y=trY[idx] |
| pb=fit_pred(raw_tr[idx],y,raw_te,"bag"); acc["raw_bagging"].append(f1_score(teY,(pb>=.5).astype(int),zero_division=0)) |
| pr=fit_pred(raw_tr_s[idx],y,raw_te_s,"lr"); acc["raw_lr"].append(f1_score(teY,(pr>=.5).astype(int),zero_division=0)) |
| pw=fit_pred(R_tr_s[idx],y,R_te_s,"lr"); acc["wm_lr"].append(f1_score(teY,(pw>=.5).astype(int),zero_division=0)) |
| pc=fit_pred(combo_tr[idx],y,combo_te,"lr"); acc["combo_lr"].append(f1_score(teY,(pc>=.5).astype(int),zero_division=0)) |
| |
| pc_tr=fit_pred(combo_tr[idx],y,combo_tr[idx],"lr") |
| cos_tr_idx=np.sum(ec[trP[idx][:,0]]*ei[trP[idx][:,1]],1) |
| fr=0.5*rankdata(pc)/len(pc)+0.5*rankdata(cos_te)/len(cos_te) |
| fr_tr=0.5*rankdata(pc_tr)/len(pc_tr)+0.5*rankdata(cos_tr_idx)/len(cos_tr_idx) |
| thr=float(np.median([np.quantile(fr_tr[y==1],.2) if (y==1).any() else .5,np.quantile(fr_tr[y==0],.8) if (y==0).any() else .5])) |
| acc["combo_lr_wmcos_fusion"].append(f1_score(teY,(fr>=thr).astype(int),zero_division=0)) |
| report[K]={k:dict(f1=round(float(np.mean(v)),4),std=round(float(np.std(v)),4)) for k,v in acc.items()} |
| print(f"\n=== K={K} ===",flush=True) |
| for k,s in sorted(report[K].items(),key=lambda kv:-kv[1]['f1']): print(f" {k:24s} F1={s['f1']:.4f} ±{s['std']:.4f}",flush=True) |
| os.makedirs(os.path.dirname(OUT),exist_ok=True); json.dump(report,open(OUT,"w"),indent=2); print("\nSaved ->",OUT,flush=True) |
|
|
